Short update on JKernelMachines with few news features (new non-convex SVM algorithm, customizable MKL regarding internal SVM solver), many bug fixes, and a more complete example usable as a standalone application.

We have a paper on image categorization accepted at ICPR next November. This is the other part of the Work Romain Negrel has been doing with VLAT. This time it's about efficiency in image classification. We tried to put every tricks of latest image categorization techniques (like dense sampling, spatial pyramid, and so on) into our VLAT while still retaining small size signatures.

All in all, we managed to achieve 61.5% mAP on VOC2007, which is not bad at all considering we used a single feature and a linear classifier (a stochastic gradient descent from Leon Bottou). Actually, if you put the throttle a bit further, you can expect better results, but then it becomes very heavy computationally speaking. As usual, some code is available here, although it's only for the Holydays dataset right now. At least you can produce the features and then use your own machine learning library (or mine, of course!).